The University of Southampton
University of Southampton Institutional Repository

Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study

Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study
Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study
This article reports how 18 UK and Canadian population health artificial intelligence researchers in Higher Education Institutions perceive the use of artificial intelligence systems in their research, and how this compares with their perceptions about the media portrayal of artificial intelligence systems. This is triangulated with a small scoping analysis of how UK and Canadian news articles portray artificial intelligence systems associated with health research and care. Interviewees had concerns about what they perceived as sensationalist reporting of artificial intelligence systems – a finding reflected in the media analysis. In line with Pickersgill’s concept of ‘epistemic modesty’, they considered artificial intelligence systems better perceived as non-exceptionalist methodological tools that were uncertain and unexciting. Adopting ‘epistemic modesty’ was sometimes hindered by stakeholders to whom the research is disseminated, who may be less interested in hearing about the uncertainties of scientific practice, having implications on both research and policy.
AI, artificial intelligence, digital data, expectations, health, health technology, hype, interviews, media, newspaper, qualitative research
0963-6625
Samuel, Gabrielle
66af6213-08de-4c0e-92c1-12083ec456e3
Diedericks, Lienkie
2135b09b-823f-4d7b-b2c9-c5e41dfe3a29
Derrick, Gemma
9403a4d9-e3f2-40d9-9483-7fcad8523468
Samuel, Gabrielle
66af6213-08de-4c0e-92c1-12083ec456e3
Diedericks, Lienkie
2135b09b-823f-4d7b-b2c9-c5e41dfe3a29
Derrick, Gemma
9403a4d9-e3f2-40d9-9483-7fcad8523468

Samuel, Gabrielle, Diedericks, Lienkie and Derrick, Gemma (2020) Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study. Public Understanding of Science. (doi:10.1177/0963662520965490).

Record type: Article

Abstract

This article reports how 18 UK and Canadian population health artificial intelligence researchers in Higher Education Institutions perceive the use of artificial intelligence systems in their research, and how this compares with their perceptions about the media portrayal of artificial intelligence systems. This is triangulated with a small scoping analysis of how UK and Canadian news articles portray artificial intelligence systems associated with health research and care. Interviewees had concerns about what they perceived as sensationalist reporting of artificial intelligence systems – a finding reflected in the media analysis. In line with Pickersgill’s concept of ‘epistemic modesty’, they considered artificial intelligence systems better perceived as non-exceptionalist methodological tools that were uncertain and unexciting. Adopting ‘epistemic modesty’ was sometimes hindered by stakeholders to whom the research is disseminated, who may be less interested in hearing about the uncertainties of scientific practice, having implications on both research and policy.

This record has no associated files available for download.

More information

Accepted/In Press date: 9 September 2020
e-pub ahead of print date: 21 October 2020
Additional Information: Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The authors received a Seed Award in Humanities and Social Science from the Wellcome Trust for the project entitled ‘The ethical governance of artificial intelligence health research in higher education institutions’, grant number: 213619/Z/18/Z/. Publisher Copyright: © The Author(s) 2020.
Keywords: AI, artificial intelligence, digital data, expectations, health, health technology, hype, interviews, media, newspaper, qualitative research

Identifiers

Local EPrints ID: 452150
URI: http://eprints.soton.ac.uk/id/eprint/452150
ISSN: 0963-6625
PURE UUID: 48d70b30-ddbd-4c78-9696-db6b453f335a

Catalogue record

Date deposited: 25 Nov 2021 21:39
Last modified: 16 Mar 2024 10:28

Export record

Altmetrics

Contributors

Author: Lienkie Diedericks
Author: Gemma Derrick

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×